Mr. chao zheng | computer science | Best Researcher Award

Mr. chao zheng | computer science | Best Researcher Award

Mr. chao zheng, manager, tencent, China.

Chao Zhen is a leading researcher in computer vision and artificial intelligence, currently heading the Computer Vision Research team at Tencent Map. He is widely recognized for his expertise in autonomous driving and machine perception. Over the years, he has driven innovation in 3D perception and semantic understanding within autonomous systems. His work regularly appears in prestigious conferences such as AAAI, ICCV, ECCV, and WACV. With a growing impact in AI and computer vision, he continues to push the boundaries of real-world applications. His collaborative research has earned accolades like the IAAI Application Innovation Award.

Publication Profile

Scopus

Google Scholar

๐ŸŽ“ Education Background

Chao Zhen holds a solid academic foundation in artificial intelligence and computer vision. While specific institutional details of his degrees are not publicly listed, his prolific publication record in high-impact conferences like ICCV, ECCV, and AAAI indicates deep formal training, likely at top-tier universities or research institutes. His education has equipped him with advanced theoretical and practical knowledge in machine learning, 3D scene understanding, and multimodal AIโ€”forming the cornerstone of his success in autonomous driving research. Through continuous learning and collaboration, he has established himself as a technical leader in AI and robotics.

๐Ÿ’ผ Professional Experience

Chao Zhen currently leads the Computer Vision Research team at Tencent Map, focusing on enabling intelligent mapping and scene understanding for autonomous vehicles. His professional journey spans several years of active involvement in cutting-edge research and development of AI-powered vision systems. Under his leadership, the team contributes to next-gen perception modules and vision-language systems for driving environments. He actively collaborates with academic and industrial partners, guiding projects from prototype to deployment. His role integrates both technical depth and strategic foresight in aligning AI research with scalable real-world applications.

๐Ÿ† Awards and Honors

Chao Zhenโ€™s outstanding contributions have been recognized with several prestigious honors, most notably the IAAI Application Innovation Award, awarded for impactful AI-driven applications. His co-authored work has gained traction in premier AI and computer vision conferences, a testament to its relevance and innovation. These accolades highlight his contributions to advancing practical autonomous driving solutions using sophisticated machine perception models. Beyond awards, his publications continue to receive high citation counts, reflecting his influence in the research community and his pivotal role in shaping the future of AI-driven transportation systems.

๐Ÿ”ฌ Research Focus

Chao Zhen’s research centers around artificial intelligence, computer vision, and machine learning, with a strong focus on 3D perception and reconstruction for autonomous driving. His work bridges data-driven learning techniques with real-world challenges, such as lidar-based segmentation, topological reasoning, and vision-language integration. He explores multimodal systems that combine point cloud data, semantic maps, and language to build robust scene understanding. Through projects like MapLM and 2DPASS, he advances scalable solutions for urban mobility. His innovations pave the way for safer, smarter, and more interpretable autonomous systems leveraging the synergy of AI modalities.

๐Ÿ“Œ Conclusion

Chao Zhen stands out as a forward-thinking AI researcher and industry leader in the realm of autonomous driving. His innovative vision and commitment to research excellence have resulted in influential publications, impactful industry contributions, and prestigious recognitions. By fusing deep technical insights with real-world needs, he is helping shape the next generation of intelligent vehicles. His ongoing efforts in 3D scene understanding, multimodal AI, and semantic modeling are not only transforming how machines perceive the world but also driving the future of intelligent transportation.

๐Ÿ“š Top Publications Notes

  1. A Survey on Multimodal Large Language Models for Autonomous Driving
    Year: 2024
    Journal/Conference: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
    Cited by: 426 articles

  2. 2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
    Year: 2022
    Journal/Conference: European Conference on Computer Vision (ECCV)
    Cited by: 326 articles

  3. MapLM: A Real-World Large-Scale Vision-Language Dataset for Map and Traffic Scene Understanding
    Year: 2024
    Journal/Conference: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    Cited by: 10 articles

  4. MapLM Benchmark: Real-World Vision-Language Benchmark for Traffic Scene Understanding
    Year: 2024
    Journal/Conference: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    Cited by: 35 articles

  5. RelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning
    Year: 2025
    Journal: arXiv preprint
    Cited by: In press (citation data to be updated)

  6. Cross-Modal Semantic Transfer for Point Cloud Semantic Segmentation
    Year: 2025
    Journal: ISPRS Journal of Photogrammetry and Remote Sensing
    Cited by: 1 article

  7. Topo2Seq: Enhanced Topology Reasoning via Topology Sequence Learning
    Year: 2025
    Journal: arXiv preprint
    Cited by: 1 article

  8. Position: Autonomous Driving & Multimodal LLMs
    Year: 2025
    Journal: Winter Conference on Applications of Computer Vision (WACV)
    Cited by: 8 articles

 

Assoc. Prof. Dr. Waleed Almuseelem | Information Security | Best Researcher Award

Assoc. Prof. Dr. Waleed Almuseelem | Information Security | Best Researcher Award

University of Tabuk, Saudi Arabia

Dr. Waleed Mohammad Hamad Almuseelem is a distinguished scholar and IT professional from Saudi Arabia, currently serving as an Associate Professor at the University of Tabuk. With a deep-rooted passion for information security, cloud computing, and cybersecurity, he seamlessly blends theoretical knowledge with practical applications. Fluent in Arabic, English, and Chinese, he is committed to advancing digital transformation and cybersecurity solutions. His entrepreneurial mindset and technical expertise contribute to both academia and industry, fostering innovation in IT and computer science. ๐ŸŒ๐Ÿ’ป

Publication Profile

๐ŸŽ“ Academic Background

Dr. Almuseelem earned his Ph.D. in Computer Science and Technology with a specialization in Information Security from Wuhan University of Technology, China, in 2017, achieving an “Excellent” evaluation. His dissertation focused on enhancing data security and user privacy in cloud computing. Prior to this, he completed a Masterโ€™s in Computer Science and Technology from the same university in 2012, working on secure frameworks for Java smart cards. His academic journey began with a Bachelor’s in Computer Technical Engineering from Riyadh College of Technology in 2007. ๐Ÿ“š๐ŸŽ“

๐Ÿ’ผ Professional Experience

With over 15 years of experience, Dr. Almuseelem has played significant roles in academia and industry. Since 2018, he has been an Associate Professor at the University of Tabuk, teaching undergraduate and master’s courses in IT, Computer Science, and Information Security. He also holds leadership roles, including Chairman of the Supervisory Committee for Foreign Languages and Member of the Quality and Academic Accreditation Committee. Previously, he worked with STC as a systems administrator and served in the Royal Saudi Air Defense Forces as a network administrator and technical support specialist. His expertise spans cloud security, Linux administration, and IT governance. ๐Ÿข๐Ÿ’ก

๐Ÿ† Awards and Honors

Dr. Almuseelem has received notable recognitions, including the Best Graduation Dissertation Award in Information Security from Wuhan University of Technology in 2012. His contributions to research and academia are acknowledged in numerous international conferences and workshops, where he shares insights on cybersecurity and open-source technologies. He is an invited speaker at global IT events and collaborates with leading institutions in cybersecurity research. ๐Ÿ…๐ŸŒŸ

๐Ÿ”ฌ Research Focus

Dr. Almuseelem’s research is centered on cloud security, intrusion detection, zero-trust architectures, and edge computing. He has contributed innovative solutions to data privacy, lightweight authentication, and IoT security. His work integrates AI and cybersecurity to tackle modern challenges in cloud and edge computing environments. Through national and international projects, he aims to develop robust security frameworks for healthcare 4.0, industrial IoT, and enterprise networks. His research is widely cited, influencing global cybersecurity standards. ๐Ÿ”๐Ÿ›ก๏ธ

๐Ÿ”— Top Publications

Intrusion Detection Systems in Cloud Environment โ€“ Mastering Intrusion Detection for Cybersecurity, IntechOpen, 2025.

A Novel Fuzzing for RPL Network Vulnerability Analysis and Vision Transformer-based Attack Detection for IIoT โ€“ Journal of Harbin Engineering University, 2024.

Continuous and Mutual Lightweight Authentication for Zero-Trust Architecture-Based Security Framework in Cloud-Edge Computing-Based Healthcare 4.0 โ€“ Journal of Theoretical and Applied Information Technology, 2023.

Joint Devices Energy and Data Security for Task Offloading in Multi-Tier Edge-Cloud Computing Systems โ€“ IEEE Access, 2023.

Data Privacy and Security Model in Cloud Environment โ€“ International Conference on Computing and Information Technology (ICCIT), 2023.

User Privacy and Security in Cloud Computing โ€“ International Journal of Security and Its Applications, 2016.

Evaluation of Data Security and Privacy Faults in Cloud Computing โ€“ EDCAV Conference, 2016.

Data Security and Privacy in Cloud Computing โ€“ Advanced Materials Research, 2014.

๐Ÿ“ข Conclusion

Dr. Waleed Almuseelem is a visionary IT educator, researcher, and cybersecurity expert dedicated to shaping the future of digital security. His contributions to cloud security, intrusion detection, and zero-trust architectures have been recognized globally. Through academic leadership, research, and industry collaborations, he continues to advance cybersecurity innovations that benefit academia, industry, and national security. ๐Ÿš€

Zongbao Jiang | Cybersecurity | Best Researcher Award

Mr. Zongbao Jiang | Cybersecurity | Best Researcher Award

Under postgraduate, Engineering University of People’s Armed Police, China

๐Ÿ“˜ Zongbao Jiang is an emerging researcher specializing in computer technology at the Engineering University of People’s Armed Police. His research focuses on reversible data hiding techniques, aiming to improve embedding capacity, security, and applicability. Through innovative methods, Jiang enhances data hiding performance, ensuring the integrity and confidentiality of original content. Actively collaborating with peers and participating in workshops, he stays abreast of the latest advancements in his field.

Profile

Scopus

 

๐ŸŽ“ Education:

Zongbao Jiang is currently an undergraduate at the Engineering University of People’s Armed Police, where he delves into computer technology and data security. His academic journey is marked by rigorous research and a strong foundation in information security.

๐Ÿ’ผ Experience:

Zongbao Jiang has participated in a project funded by the National Natural Science Foundation of China, collaborating with notable researchers like Minqing Zhang. He has successfully published papers in top-tier journals and conferences, demonstrating his expertise and contribution to the field of computer technology.

๐Ÿ”ฌ Research Interests:

Zongbao Jiang’s research interests revolve around information security and reversible data hiding techniques. His work focuses on enhancing performance metrics such as embedding capacity and security while maintaining the confidentiality of original content. Jiang’s innovative approach aims to develop robust solutions for secure communications and data preservation.

๐Ÿ† Awards:

Zongbao Jiang has made significant contributions to his field, evidenced by his publications in high-impact journals and conferences. He holds three authorized software copyrights and has a patent under review. His work in reversible data hiding techniques has earned him recognition in the academic community.

Publications

Reversible Data Hiding Algorithm in Encrypted Images Based on Adaptive Median Edge Detection and Matrix-Based Secret Sharing
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Reversible Data Hiding in Encrypted Images based on Classic McEliece Cryptosystem
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Reversible Data Hiding Algorithm in Encrypted Domain Based on Matrix Secret Sharing
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